I'm doing an image classification task and the number of features of each example image is pretty huge (3,072: # pixels in each image). I'm thinking of using PCA to reduce the # features of each image to $n$ (say $n = 100$), and then use SVM to to learn and classify using the reduced feature space. I'm wondering which of these two paths should I follow?

1. PCA on the training set $T$ and use SVM to learn on new $T$ with reduced dimensions. For prediction, PCA again on the test set $S$ and use the learned SVM parameters to classify.

2. PCA on both $T$ and $S$ at the same time. That is, merge $T+S$ into a large matrix and perform PCA on them. Then split the reduced dimension matrix into $T$ and $S$ again. Learn the SVM on the reduced $T$ and then use the learned SVM to predict on the reduced $S$.